Abstract:

Abstract: More Electric Aircraft (MEA) need sophisticated methods to regulate power quality, especially harmonic distortion, because to their growing dependence on electrical power sources. In this research, we investigate the potential of DC-DC converters powered by Artificial Neural Networks (ANNs) to actively reduce harmonic interference in MEA settings. To guarantee that power conversion systems adhere to strict electromagnetic compatibility (EMC) standards, the suggested approach makes use of ANNs' adaptive learning capabilities to enhance harmonic filtering efficiency. Optimizing system performance under varied load and operating situations is achieved by the effective identification and mitigation of harmonic frequencies by the trained ANN model. Improved system dependability and decreased power losses are two outcomes of the drastically reduced harmonic distortion seen in the simulation findings. The research shows that ANN-based DC-DC converters can maintain power quality in MEAs efficiently and scalable, making them a good option for current aerospace applications.

Description:

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